Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

CHRONIC LYMPHOCYTIC LEUKEMIA

A T cell inflammatory phenotype is associated with autoimmune toxicity of the PI3K inhibitor duvelisib in chronic lymphocytic leukemia

Leukemia volume 36, pages 723–732 (2022)Cite this article

Subjects

Abstract

Several PI3Kδ inhibitors are approved for the therapy of B cell malignancies, but their clinical use has been limited by unpredictable autoimmune toxicity. We have recently reported promising efficacy results in treating chronic lymphocytic leukemia (CLL) patients with combination therapy with the PI3Kδγ inhibitor duvelisib and fludarabine cyclophosphamide rituximab (FCR) chemoimmunotherapy, but approximately one-third of patients develop autoimmune toxicity. We show here that duvelisib FCR treatment in an upfront setting modulates both CD4 and CD8 T cell subsets as well as pro-inflammatory cytokines. Decreases in naive and central memory CD4 T cells and naive CD8 T cells occur with treatment, while activated CD8 T cells, granzyme positive Tregs, and Th17 CD4 and CD8 T cells all increase with treatment, particularly in patients with toxicity. Cytokines associated with Th17 activation (IL-17A and IL-21) are also relatively elevated in patients with toxicity. The only CLL feature associated with toxicity was increased priming for apoptosis at baseline, with a significant decrease during the first week of duvelisib. We conclude that an increase in activated CD8 T cells with activation of Th17 T cells, in the context of lower baseline Tregs and greater CLL resistance to duvelisib, is associated with duvelisib-related autoimmune toxicity.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of CyTOF data from all patients.
Fig. 2: Overview of changing T cell clusters in patients with and without toxicity.
Fig. 3: Decrease in naive CD4 and central memory CD4s in patients with toxicity.
Fig. 4: Increase in activated CD8 T cells, activated Th17 CD8 T cells and decrease in naive CD8s identified in patients with toxicity.
Fig. 5: Changes in Th17s and Tregs with dFCR treatment as shown with mass cytometry.
Fig. 6: Pro-inflammatory cytokines decrease with treatment in patients without toxicity on dFCR treatment.
Fig. 7: CLL cells from patients with toxicity show increased priming at baseline, with a decrease after one week of duvelisib.

Similar content being viewed by others

References

  1. Lampson BL, Kasar SN, Matos TR, Morgan EA, Rassenti L, Davids MS, et al. Idelalisib given front-line for treatment of chronic lymphocytic leukemia causes frequent immune-mediated hepatotoxicity. Blood. 2016;128:195–203.

    Article  CAS  Google Scholar 

  2. Sharman JP, Coutre SE, Furman RR, Cheson BD, Pagel JM, Hillmen P, et al. Final results of a randomized, phase iii study of rituximab with or without idelalisib followed by open-label idelalisib in patients with relapsed chronic lymphocytic leukemia. J Clin Oncol. 2019;37:1391–402.

    Article  CAS  Google Scholar 

  3. Winkler DG, Faia KL, DiNitto JP, Ali JA, White KF, Brophy EE, et al. PI3K-delta and PI3K-gamma inhibition by IPI-145 abrogates immune responses and suppresses activity in autoimmune and inflammatory disease models. Chem Biol. 2013;20:1364–74.

    Article  CAS  Google Scholar 

  4. Flinn IW, Hillmen P, Montillo M, Nagy Z, Illes A, Etienne G, et al. The phase 3 DUO trial: duvelisib vs ofatumumab in relapsed and refractory CLL/SLL. Blood. 2018;132:2446–55.

    Article  CAS  Google Scholar 

  5. Flinn IW, Miller CB, Ardeshna KM, Tetreault S, Assouline SE, Mayer J, et al. DYNAMO: a phase II study of duvelisib (IPI-145) in patients with refractory indolent non-Hodgkin lymphoma. J Clin Oncol. 2019;37:912–22.

    Article  CAS  Google Scholar 

  6. Kaneda MM, Messer KS, Ralainirina N, Li H, Leem CJ, Gorjestani S, et al. PI3Kgamma is a molecular switch that controls immune suppression. Nature. 2016;539:437–42.

    Article  CAS  Google Scholar 

  7. Brown JR, Zelenetz A, Furman R, Lamanna N, Mato A, Montillo M, et al. Risk factors for grade 3/4 transaminase elevation in patients with chronic lymphocytic leukemia treated with idelalisib. Leukemia. 2020;34:3404–7.

    Article  CAS  Google Scholar 

  8. Davids MS, Fisher DC, Tyekucheva S, McDonough M, Hanna J, Lee B, et al. A phase 1b/2 study of duvelisib in combination with FCR (DFCR) for frontline therapy for younger CLL patients. Leukemia. 2021;35:1064–72.

    Article  CAS  Google Scholar 

  9. Zunder ER, Finck R, Behbehani GK, Amir el AD, Krishnaswamy S, Gonzalez VD, et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat Protoc. 2015;10:316–33.

    Article  CAS  Google Scholar 

  10. Finck R, Simonds EF, Jager A, Krishnaswamy S, Sachs K, Fantl W, et al. Normalization of mass cytometry data with bead standards. Cytom A. 2013;83:483–94.

    Article  Google Scholar 

  11. Bagwell CB, Inokuma M, Hunsberger B, Herbert D, Bray C, Hill B, et al. Automated data cleanup for mass. Cytom Cytom A. 2020;97:184–98.

    Article  CAS  Google Scholar 

  12. Chevrier S, Crowell HL, Zanotelli VRT, Engler S, Robinson MD, Bodenmiller B. Compensation of signal spillover in suspension and imaging mass cytometry. Cell Syst. 2018;6:612–620 e615.

    Article  CAS  Google Scholar 

  13. van der Maaten L. Accelerating t-SNE using Tree-Based Algorithms. J Mach Learn Res. 2014;15:1–21.

    Google Scholar 

  14. Rodriguez A, Laio A. Machine learning. Clustering fast search find density peaks Sci. 2014;344:1492–6.

    CAS  Google Scholar 

  15. Chen H, Lau MC, Wong MT, Newell EW, Poidinger M, Chen J. Cytofkit: a bioconductor package for an integrated mass cytometry data analysis pipeline. PLoS Comput Biol. 2016;12:e1005112.

    Article  Google Scholar 

  16. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

    Article  CAS  Google Scholar 

  17. Yu N, Li X, Song W, Li D, Yu D, Zeng X, et al. CD4(+)CD25 (+)CD127 (low/-) T cells: a more specific Treg population in human peripheral blood. Inflammation. 2012;35:1773–80.

    Article  Google Scholar 

  18. Ivanov II, McKenzie BS, Zhou L, Tadokoro CE, Lepelley A, Lafaille JJ, et al. The orphan nuclear receptor RORgammat directs the differentiation program of proinflammatory IL-17+ T helper cells. Cell. 2006;126:1121–33.

    Article  CAS  Google Scholar 

  19. Gadi D, Kasar S, Griffith A, Chiu PY, Tyekucheva S, Rai V, et al. Imbalance in T cell subsets triggers the autoimmune toxicity of PI3K inhibitors in CLL. Blood. 2019;134:1745.

    Article  Google Scholar 

  20. Billerbeck E, Kang YH, Walker L, Lockstone H, Grafmueller S, Fleming V, et al. Analysis of CD161 expression on human CD8+ T cells defines a distinct functional subset with tissue-homing properties. Proc Natl Acad Sci USA. 2010;107:3006–11.

    Article  Google Scholar 

  21. Sula Karreci E, Eskandari SK, Dotiwala F, Routray SK, Kurdi AT, Assaker JP, et al. Human regulatory T cells undergo selfinflicted damage via granzyme pathways upon activation. JCI Insight. 2017;2:e91599 https://doi.org/10.1172/jci.insight.91599

    Article  PubMed Central  Google Scholar 

  22. Vartanov A, Matos T, McWilliams E, Gadi D, Rao D, Kasar S, et al. Mass cytometry identifies T cell populations associated with severe hepatotoxicity in CLL patients on upfront idelalisib. Blood. 2018;132:4413.

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank all the patients who participated in this study and contributed their samples. The study is funded by NIH RO1 CA 213442 (PI: Brown, Jennifer) and Verastem Oncology. The study was funded by NIH RO1 CA 213442 (PI: Brown, Jennifer), Verastem Oncology (Brown, Jennifer), and NIH U01AI138318, P30AR070253, P30AR069625 (Lederer, James, A).

Funding

NIH RO1 CA 213442 (Brown, Jennifer); Verastem Oncology (Brown, Jennifer); NIH U01AI138318, P30AR070253, P30AR069625 (Lederer, James, A).

Author information

Author notes

  1. These authors contributed equally: Deepti Gadi, Alec Griffith.

Authors and Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Deepti Gadi, Vanessa Rai, Alexander Vartanov, Emily Thrash, Stacey M. Fernandes, Timothy Z. Lehmberg, Brandon Lee, Stephen P. Martindale, John-Hanson Machado, Oreofe Odejide, Philippe Armand, David C. Fisher, Matthew S. Davids & Jennifer R. Brown

  2. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Deepti Gadi, Vanessa Rai, Emily Thrash, Stacey M. Fernandes, Stephen P. Martindale, John-Hanson Machado, Oreofe Odejide, Philippe Armand, David C. Fisher, Matthew S. Davids & Jennifer R. Brown

  3. Department of Surgery, Brigham and Women’s Hospital, Boston, MA, USA

    Alec Griffith & James A. Lederer

  4. Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA

    Svitlana Tyekucheva & Zixu Wang

  5. Department of Medical Oncology, Beth Israel Deaconess Medical Center, Boston, USA

    Jon Arnason

Authors
  1. Deepti Gadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alec Griffith
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Svitlana Tyekucheva
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zixu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vanessa Rai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alexander Vartanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Emily Thrash
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Stacey M. Fernandes
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Timothy Z. Lehmberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Brandon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Stephen P. Martindale
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. John-Hanson Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Oreofe Odejide
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Philippe Armand
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. David C. Fisher
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Jon Arnason
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Matthew S. Davids
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. James A. Lederer
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Jennifer R. Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Research conception and design: DG, AG, MSD, JAL, JRB. Performed research and collected data: DG, AG, VR, ET, TZL, MSD, JAL, JRB. Enrolled patients: OO, PA, DCF, JA, MSD, JRB. Analyzed, interpreted and performed statistical analysis: DG, AG, ST, ZW, SPM, JAL, JRB. Wrote the manuscript: first draft DG and JRB; all authors revised and approved the final. Administrative support (i.e., bio-banking, managing and organizing samples): VR, AV, SMF, BL, JHM. Study supervision: JAL, JRB.

Corresponding author

Correspondence to Jennifer R. Brown.

Ethics declarations

Competing interests

ET has received travel expenses and honorariums from Fluidigm and is a current employee of Fluidigm. TZL is a current employee of Casma Therapeutics. PA has served as a consultant for Merck, BMS, Pfizer, Affimed, Adaptive, Infinity, ADC Therapeutics, Celgene, Morphosys, Daiichi Sankyo, Miltenyi, Tessa, GenMab, C4, Enterome, Regeneron, Epizyme, Astra Zeneca, and Genentech; received research funding from Merck, BMS, Affimed, Adaptive, Roche, Tensha, Otsuka, Sigma Tau, Genentech, IGM, and Kite; and received honoraria from Merck and BMS. DCF served on the advisory board for Kyowa Kirin. JA served on the advisory boards for Regeneron and BMS/Celgene. MSD has received research funding from AbbVie, Ascentage Pharma, AstraZeneca, Genentech, MEI Pharma, Novartis, Pharmacyclics, Surface Oncology, TG Therapeutics, and Verastem; has served on the advisory board for AbbVie, Adaptive Biotechnologies, Ascentage Pharma, AstraZeneca, BeiGene, Celgene, Eli Lilly, Janssen, Pharmacyclics, Takeda, and TG Therapeutics; has served as a consultant for AbbVie, Adaptive Biotechnologies, AstraZeneca, BeiGene, Genentech, Janssen, Merck, Pharmacyclics, Research to Practice, Syros Pharmaceuticals, TG Therapeutics, Verastem, and Zentalis. JAL consults for Alloplex Biotherapeutics. JRB has served as a consultant for Abbvie, Acerta/Astra-Zeneca, Beigene, Bristol Myers Squibb/Juno/Celgene, Catapult, Dynamo, Eli Lilly, Genentech/Roche, Gilead, Janssen, Kite, Loxo, MEI Pharma, Morphosys AG, Nextcea, Novartis, Octapharma, Rigel, Pfizer, Pharmacyclics, Redx, Sun, Sunesis, TG Therapeutics, Verastem; received honoraria from Janssen and Teva; received research funding from Gilead, Loxo/Lilly, Sun and Verastem/SecuraBio, and TG Therapeutics; and served on data safety monitoring committees for Morphosys and Invectys. DG, AG, ST, ZW, VR, AV, SMF, BL, SPM, JHM and OO have no COI to disclose.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gadi, D., Griffith, A., Tyekucheva, S. et al. A T cell inflammatory phenotype is associated with autoimmune toxicity of the PI3K inhibitor duvelisib in chronic lymphocytic leukemia. Leukemia 36, 723–732 (2022). https://doi.org/10.1038/s41375-021-01441-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41375-021-01441-9

This article is cited by

Search

Advanced search

Quick links